{"id":"xu-2015-bgb-d","source":{"id":"xu-2015","citation":"Xu, Y., Zhang, J., Franklin, S. B., et al. (2015). Improving allometry models to estimate the above- and belowground biomass of subtropical forest, China. Ecosphere, 6(12), 289.","title":"Improving allometry models to estimate the above- and belowground biomass of subtropical forest, China","authors":["Xu, Y.","Zhang, J.","Franklin, S. B."],"year":2015,"venue":"Ecosphere","doi":"10.1890/es15-00198.1","url":"https://doi.org/10.1890/es15-00198.1","read_url":"https://esajournals.onlinelibrary.wiley.com/doi/full/10.1890/ES15-00198.1","access":"open","license":"CC BY 3.0","checked_at":"2026-11-02","status":"current"},"name":"Subtropical China root biomass from D","output":{"quantity":"bgb"},"expression":"exp(-2.80346 + 2.0441 * ln(D))","published":{"expression":"exp(-2.80346 + 2.0441 * ln(D))","units":{"D":"cm"},"output_unit":"kg"},"taxon":{"level":"mixed","name":"8 subtropical forest tree species (23 root systems)","group":"mixed"},"region":{"scope":"site","countries":["CN"],"place":"Badagongshan National Nature Reserve, Hunan province","biomes":["subtropical","montane"]},"stand":"natural","fit":{"n_trees":23,"r2":0.9241,"rse":0.3437,"method":"Bayesian log-log regression (MCMC)"},"evidence":[{"field":"expression","quote":"BGB = exp(−2.80346 + 2.0441ln(D))","locator":"BGB models with diameter, p. 8"}],"check":[{"inputs":{"D":10},"expected":6.7077,"origin":"computed","note":"computed from the printed coefficients"},{"inputs":{"D":30},"expected":63.366,"origin":"computed","note":"computed from the printed coefficients"}],"verification":"original_read","checked_at":"2026-11-02","notes":"Log-log model fitted by Bayesian MCMC; no correction factor is stated, so none is applied. Natural log assumed from the paper's ln notation. Wood density WD is in g cm-3 (Table 2; the running text says g/m3). Whole-tree AGB includes branches, leaves and fruit. Fitted on 23 excavated root systems of eight species (large roots, small roots and stump). The residual standard error given is 0.3437 on the log scale and R² is 0.9241."}